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Vol. 3 No. 1 (2026) • Articles
Open Access

Pengklasifikasian Jenis Sampah Berbasis Visi Komputer Dan Kecerdasan Buatan

Gusti Made Kresna Wijaya Wijaya
Politeknik Manufaktur Bandung
Daffa Khairul Ammar
Politeknik Manufaktur Bandung
Published: March 28, 2026 Pages: 1-10
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Abstract

Waste management presents a significant challenge in ensuring environmental sustainability, requiring an automated classification system to improve efficiency. This study designs a waste classification system (biological, electronic, glass, plastic) using a deep learning approach based on computer vision. The proposed method implements a custom Convolutional Neural Network (CNN) with MobileNet efficiency principles, consisting of Mobile Inverted Bottleneck Convolution (MBConv) and Squeeze-and-Excitation (SE) blocks. The model is developed from scratch using a four-class dataset and optimized with GPU processing and a batch size of 16. After fine-tuning the regularization and hyperparameters, the model achieved the highest accuracy of 75.59%.

Keywords:
Classification CNN Waste Architecture
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Author Biographies
Gusti Made Kresna Wijaya Wijaya Politeknik Manufaktur Bandung

Program Studi Teknologi Rekayasa Informatika Industri, Automation Engineering, Politeknik Manufaktur Bandung, Kota Bandung, Provinsi Jawa Barat, Indonesia.

Daffa Khairul Ammar Politeknik Manufaktur Bandung

Program Studi Teknologi Rekayasa Informatika Industri, Automation Engineering, Politeknik Manufaktur Bandung, Kota Bandung, Provinsi Jawa Barat, Indonesia.

How to Cite
Wijaya, G. M. K. W., & Ammar, D. K. (2026). Pengklasifikasian Jenis Sampah Berbasis Visi Komputer Dan Kecerdasan Buatan. Jurnal Ilmu Komputer Dan Teknologi Informasi, 3(1), 1-10. https://doi.org/10.63447/jikti.v3i1.1729
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